Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenueHigh correlation
avg_unique_basket_size is highly overall correlated with quantity and 2 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664)Skewed
frequency is highly skewed (γ1 = 24.87687084)Skewed
qtde_returns is highly skewed (γ1 = 21.9754032)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
qtde_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-09-05 18:55:06.064003
Analysis finished2023-09-05 18:55:27.814129
Duration21.75 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:28.014604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1445
Coefficient of variation (CV)0.11258036
Kurtosis-1.2061782
Mean15270.377
Median Absolute Deviation (MAD)1489
Skewness0.032193711
Sum45322479
Variance2955457.9
MonotonicityNot monotonic
2023-09-05T15:55:28.381865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12670 1
 
< 0.1%
17734 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.4851
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:28.546254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.465
Coefficient of variation (CV)3.7629558
Kurtosis397.30132
Mean2693.4851
Median Absolute Deviation (MAD)670.84
Skewness17.635372
Sum7994263.7
Variance1.0272766 × 108
MonotonicityNot monotonic
2023-09-05T15:55:28.698462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
1353.74 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.309299
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:28.866997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.760922
Coefficient of variation (CV)1.2091707
Kurtosis2.7765172
Mean64.309299
Median Absolute Deviation (MAD)26
Skewness1.7980529
Sum190870
Variance6046.7611
MonotonicityNot monotonic
2023-09-05T15:55:29.051760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7243935
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:29.223704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8577599
Coefficient of variation (CV)1.5473709
Kurtosis190.78624
Mean5.7243935
Median Absolute Deviation (MAD)2
Skewness10.765555
Sum16990
Variance78.45991
MonotonicityNot monotonic
2023-09-05T15:55:29.375913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

quantity
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76449
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:29.563671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.93294
Coefficient of variation (CV)2.1987868
Kurtosis354.77884
Mean122.76449
Median Absolute Deviation (MAD)44
Skewness15.706135
Sum364365
Variance72863.79
MonotonicityNot monotonic
2023-09-05T15:55:29.732814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2628
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

qtde_products
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76449
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:29.918321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.93294
Coefficient of variation (CV)2.1987868
Kurtosis354.77884
Mean122.76449
Median Absolute Deviation (MAD)44
Skewness15.706135
Sum364365
Variance72863.79
MonotonicityNot monotonic
2023-09-05T15:55:30.092800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2628
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.994257
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:30.327693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.118111
median17.953447
Q324.981794
95-th percentile90.052125
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.863683

Descriptive statistics

Standard deviation119.53207
Coefficient of variation (CV)3.6228143
Kurtosis812.96474
Mean32.994257
Median Absolute Deviation (MAD)5.9790186
Skewness25.156966
Sum97926.954
Variance14287.915
MonotonicityNot monotonic
2023-09-05T15:55:30.511972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-67.302133
Minimum-366
Maximum-1
Zeros0
Zeros (%)0.0%
Negative2968
Negative (%)100.0%
Memory size46.4 KiB
2023-09-05T15:55:30.691752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-200.65
Q1-85.333333
median-48.267857
Q3-25.917308
95-th percentile-8
Maximum-1
Range365
Interquartile range (IQR)59.416026

Descriptive statistics

Standard deviation63.505358
Coefficient of variation (CV)-0.94358612
Kurtosis4.9080488
Mean-67.302133
Median Absolute Deviation (MAD)26.267857
Skewness-2.066084
Sum-199752.73
Variance4032.9306
MonotonicityNot monotonic
2023-09-05T15:55:30.864522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 25
 
0.8%
-4 22
 
0.7%
-70 21
 
0.7%
-7 20
 
0.7%
-35 19
 
0.6%
-49 18
 
0.6%
-11 17
 
0.6%
-46 17
 
0.6%
-21 17
 
0.6%
-28 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
-366 1
 
< 0.1%
-365 1
 
< 0.1%
-363 1
 
< 0.1%
-362 1
 
< 0.1%
-357 2
0.1%
-356 1
 
< 0.1%
-355 2
0.1%
-352 1
 
< 0.1%
-351 2
0.1%
-350 3
0.1%
ValueCountFrequency (%)
-1 16
0.5%
-1.5 1
 
< 0.1%
-2 13
0.4%
-2.5 1
 
< 0.1%
-2.601398601 1
 
< 0.1%
-3 15
0.5%
-3.321428571 1
 
< 0.1%
-3.330357143 1
 
< 0.1%
-3.5 2
 
0.1%
-4 22
0.7%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11383237
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:31.057437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016339869
median0.025898352
Q30.049478583
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033138713

Descriptive statistics

Standard deviation0.40822056
Coefficient of variation (CV)3.5861552
Kurtosis989.06632
Mean0.11383237
Median Absolute Deviation (MAD)0.012196886
Skewness24.876871
Sum337.85449
Variance0.16664402
MonotonicityNot monotonic
2023-09-05T15:55:31.247621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2635
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.888477
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:31.442487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.86478
Coefficient of variation (CV)8.107685
Kurtosis596.20199
Mean34.888477
Median Absolute Deviation (MAD)1
Skewness21.975403
Sum103549
Variance80012.486
MonotonicityNot monotonic
2023-09-05T15:55:31.654112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (203) 705
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.25289
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:32.099248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.29167
Q3281.54808
95-th percentile599.58
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.31058

Descriptive statistics

Standard deviation283.8932
Coefficient of variation (CV)1.2016496
Kurtosis102.78169
Mean236.25289
Median Absolute Deviation (MAD)83.041667
Skewness7.7018777
Sum701198.57
Variance80595.347
MonotonicityNot monotonic
2023-09-05T15:55:32.291757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
73 9
 
0.3%
136 8
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
130 7
 
0.2%
Other values (1968) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.489977
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-09-05T15:55:32.527447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.144643
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.477976

Descriptive statistics

Standard deviation15.460127
Coefficient of variation (CV)0.88394209
Kurtosis29.324685
Mean17.489977
Median Absolute Deviation (MAD)6.6
Skewness3.4364678
Sum51910.252
Variance239.01552
MonotonicityNot monotonic
2023-09-05T15:55:32.722403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
17 38
 
1.3%
14 38
 
1.3%
11 36
 
1.2%
5 36
 
1.2%
7 36
 
1.2%
15 35
 
1.2%
Other values (896) 2588
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-09-05T15:55:25.626238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.314876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.991721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.810861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.616638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.357383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.212874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.945840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.504892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.143217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.010894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.815180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.759362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.447430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.132040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.972100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.769148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.685164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.352486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.074882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.643242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.285117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.156432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.979366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.906074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.578737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.274789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.116746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.911548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.821611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.486085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.199369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.775439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.418004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.295295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.129486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.055534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.725149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.426373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.264618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.058259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.970434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.639134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.336540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.918307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.756775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.458764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.299393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.179363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.845857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.549320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.399199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.179450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.094708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.794667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.455701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.045496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.881959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.598490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.454647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.323369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:06.994837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.728159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.553020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.331594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.243340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.960155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.594868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.193737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.027828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.736099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.620737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.466390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.156201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:08.890291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.710926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.509383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.392092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.109349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.729312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.342216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.176203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:22.901894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.780855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.611608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.282910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.031097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:10.849892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.651624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.517879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.242718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.849084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.472222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.311132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.034379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:24.906773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.762512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.425093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.178319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.001977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.823599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.664394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.391375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:17.991459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.610544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.459925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.210929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.049896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:26.909423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.576289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.346582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.162135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:12.970976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.812129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.541033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.128894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.754064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.599926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.376368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.213688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:27.046398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.710671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.505488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.309769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.095158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:14.941828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.671669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.245751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:19.877314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.727170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.516656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.350305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:27.209248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:07.858315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:09.654746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:11.467366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:13.227298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:15.080054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:16.810118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:18.376828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:20.012280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:21.867295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:23.666375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-09-05T15:55:25.491563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-09-05T15:55:32.873226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesquantityqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0770.0010.0260.0130.013-0.131-0.019-0.002-0.064-0.123-0.016
gross_revenue-0.0771.000-0.4140.7720.7460.7460.2450.2490.0910.3710.5740.106
recency_days0.001-0.4141.000-0.503-0.436-0.4360.049-0.1090.017-0.119-0.0970.014
qtde_invoices0.0260.772-0.5031.0000.6900.6900.0600.2580.0780.2950.101-0.181
quantity0.0130.746-0.4360.6901.0001.000-0.3770.1650.0350.2440.3840.515
qtde_products0.0130.746-0.4360.6901.0001.000-0.3770.1650.0350.2440.3840.515
avg_ticket-0.1310.2450.0490.060-0.377-0.3771.0000.1230.0910.1890.187-0.618
avg_recency_days-0.0190.249-0.1090.2580.1650.1650.1231.0000.8810.3980.078-0.131
frequency-0.0020.0910.0170.0780.0350.0350.0910.8811.0000.2350.028-0.122
qtde_returns-0.0640.371-0.1190.2950.2440.2440.1890.3980.2351.0000.209-0.053
avg_basket_size-0.1230.574-0.0970.1010.3840.3840.1870.0780.0280.2091.0000.404
avg_unique_basket_size-0.0160.1060.014-0.1810.5150.515-0.618-0.131-0.122-0.0530.4041.000

Missing values

2023-09-05T15:55:27.421191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-05T15:55:27.704015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesquantityqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.0297.0297.018.152222-35.50000017.00000040.050.9705880.617647
1130473232.5956.09.0171.0171.018.904035-27.2500000.02830235.0154.44444411.666667
2125836705.382.015.0232.0232.028.902500-23.1875000.04032350.0335.2000007.600000
313748948.2595.05.028.028.033.866071-92.6666670.0179210.087.8000004.800000
415100876.00333.03.03.03.0292.000000-8.6000000.07317122.026.6666670.333333
5152914623.3025.014.0102.0102.045.326471-23.2000000.04011529.0150.1428574.357143
6146885630.877.021.0327.0327.017.219786-18.3000000.057221399.0172.4285717.047619
7178095411.9116.012.061.061.088.719836-35.7000000.03352041.0171.4166673.833333
81531160767.900.091.02379.02379.025.543464-4.1444440.243316474.0419.7142866.230769
9160982005.6387.07.067.067.029.934776-47.6666670.0243900.087.5714294.857143
customer_idgross_revenuerecency_daysqtde_invoicesquantityqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
5626177271060.2515.01.066.066.016.064394-6.01.0000006.0645.00000066.000000
563617232421.522.02.036.036.011.708889-12.00.1538460.0101.50000015.000000
563717468137.0010.02.05.05.027.400000-4.00.4000000.058.0000002.500000
564813596697.045.02.0166.0166.04.199036-7.00.2500000.0203.00000066.500000
5654148931237.859.02.073.073.016.956849-2.00.6666670.0399.50000036.000000
565812479473.2011.01.030.030.015.773333-4.01.00000034.0382.00000030.000000
567914126706.137.03.015.015.047.075333-3.00.75000050.0169.3333334.666667
5685135211092.391.03.0435.0435.02.511241-4.50.3000000.0244.333333104.000000
569515060301.848.04.0120.0120.02.515333-1.02.0000000.065.50000020.000000
571412558269.967.01.011.011.024.541818-6.01.000000196.0196.00000011.000000